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Updated: Sep 10, 2025

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基于可见/近红外光谱技术的快速和非破坏性检测布里克斯的研究

Renhao Liu¹, Qingxu Li², Hongzhou Zhang¹

¹College of Mechanical and Electrical Engineering, Tarim University, Alar, China.

Journal of food science

|August 20, 2025

概括

这项研究引入了一种快速,非破坏性的方法,用于使用可见/近红外 (Vis/NIR) 光谱测量质量. 支持矢量回归 (SVR) 模型准确地估计了木的水平,这对于质量控制至关重要.

科学领域:

农业科学农业科学
频谱学是一种光谱学.
数据科学数据科学数据科学

背景情况:

是营养丰富的水果,含量是关键的质量指标.
准确,快速,非破坏性地测量木仍然是食品行业的一个重大挑战.

研究的目的:

开发一种快速而非破坏性的方法来估计木的含量.
用光谱数据确定最佳特征波长,以准确地预测布里克斯.

主要方法:

可见/近红外 (Vis/NIR) 光谱数据从各种类型的果中收集.
标准正常变异 (SNV) 预处理和竞争性适应性重权取样 (CARS) 用于特征选择被应用.
开发了包括部分最小平方回归 (PLSR),支向量回归 (SVR) 和最小平方支向量机器 (LSSVM) 在内的预测模型.

主要成果:

使用CARS选择的波长的SVR模型显示出最高的预测精度 (R2 = 0.8625,RPD = 2.7229,RMSE = 1.6801).
验证测试证实了该模型的可靠性,R2 = 0.8608,RPD = 2.7069和RMSE = 1.69.
这项研究成功地确定了用于普鲁恩布里克斯估计的关键波长.

结论:

拟议的Vis/NIR光谱方法与CARS和SVR相结合,提供了一种有效的方法,可以快速,非破坏性地检测PruneBrix.
这项研究为开发先进的仪器提供了宝贵的技术基础,用于快速评估的质量.

关键词:

布里克斯 (Brix) 是一个机器学习是机器学习.梅子梅子梅子快速检测快速检测迅速检测可见/近红外光谱学

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